Belief Revision Process based on Trust: Simulation Experiments

This paper introduces a multi-agent belief revision algorithm based on belief networks. The algorithm utilizes knowledge about reliability or trustworthiness of information sources to generate the most probable perspective of the system. Incorporating reliability information into belief revision mechanisms is essential for agents in real world multi-agent systems where information sources with various levels of reliability are present. Modeling the quality of incoming information is useful regardless of the level and type of security in such systems. Equipped with the proposed algorithm, an agent is capable of (1) evaluating incoming information and generating a consistent knowledge base to reason on, and (2) avoiding fraudulent information from unreliable or deceptive information sources. The performance (frequency response and correctness) of the proposed algorithm is analyzed in terms of delay time, maximum overshoot, and steady-state error.